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Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations148
Missing cells814
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 KiB
Average record size in memory328.9 B

Variable types

Text15
Categorical17
DateTime7
Numeric2

Alerts

primary_site has constant value "Other and ill-defined digestive organs" Constant
disease_type has constant value "Complex Mixed and Stromal Neoplasms" Constant
updated_datetime has constant value "2018-10-25T11:34:27.425461-05:00" Constant
project__project_id_clinical has constant value "FM-AD" Constant
diagnoses__morphology has constant value "8936/1" Constant
diagnoses__primary_diagnosis has constant value "Gastrointestinal stromal tumor, NOS" Constant
project__project_id_bio has constant value "FM-AD" Constant
samples__updated_datetime has constant value "2023-10-13T14:28:47.406440-05:00" Constant
samples__portions__analytes__aliquots__analyte_type has constant value "DNA" Constant
diagnoses__classification_of_tumor is highly overall correlated with diagnoses__site_of_resection_or_biopsy and 2 other fieldsHigh correlation
diagnoses__site_of_resection_or_biopsy is highly overall correlated with diagnoses__classification_of_tumor and 2 other fieldsHigh correlation
samples__sample_type is highly overall correlated with diagnoses__classification_of_tumor and 2 other fieldsHigh correlation
samples__tumor_descriptor is highly overall correlated with diagnoses__classification_of_tumor and 2 other fieldsHigh correlation
diagnoses__classification_of_tumor is highly imbalanced (82.1%) Imbalance
samples__tumor_descriptor is highly imbalanced (82.1%) Imbalance
samples__sample_type is highly imbalanced (82.1%) Imbalance
samples__portions__analytes__analyte_id has 74 (50.0%) missing values Missing
samples__portions__slides__updated_datetime has 74 (50.0%) missing values Missing
samples__portions__slides__submitter_id has 74 (50.0%) missing values Missing
samples__portions__slides__slide_id has 74 (50.0%) missing values Missing
samples__portions__slides__created_datetime has 74 (50.0%) missing values Missing
samples__portions__slides__percent_tumor_nuclei has 74 (50.0%) missing values Missing
samples__portions__analytes__aliquots__aliquot_id has 74 (50.0%) missing values Missing
samples__portions__analytes__aliquots__analyte_type has 74 (50.0%) missing values Missing
samples__portions__analytes__aliquots__updated_datetime has 74 (50.0%) missing values Missing
samples__portions__analytes__aliquots__submitter_id has 74 (50.0%) missing values Missing
samples__portions__analytes__aliquots__created_datetime has 74 (50.0%) missing values Missing
samples__portions__portion_id has unique values Unique

Reproduction

Analysis started2025-07-21 14:07:30.949008
Analysis finished2025-07-21 14:07:32.159662
Duration1.21 second
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

case_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:32.271495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters5328
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0171fffa-a648-4053-8c12-e0786450a030
2nd row0171fffa-a648-4053-8c12-e0786450a030
3rd row0434ffff-a75c-4c7b-bfc0-87d54b7f836e
4th row0434ffff-a75c-4c7b-bfc0-87d54b7f836e
5th row0946a9c1-d7a8-4e85-89a9-8ba47fde4f1b
ValueCountFrequency (%)
0171fffa-a648-4053-8c12-e0786450a030 2
 
1.4%
4379dbed-a137-47c1-a921-3c888dceee91 2
 
1.4%
09aacc20-afeb-4bed-96f1-cd709b0741ab 2
 
1.4%
0a373e5b-7df2-4a37-b04b-f7b0751ca6b5 2
 
1.4%
14d4136f-222e-40a2-b4b9-7b04f9828f3c 2
 
1.4%
193b7011-724c-44bc-83f2-51d69875b658 2
 
1.4%
1b4e7176-c752-4547-b95f-303b156203aa 2
 
1.4%
1d287e75-1ad0-4c23-bbd1-22a45a1e9165 2
 
1.4%
1d8d6450-e0f5-4d22-b654-0337950dead8 2
 
1.4%
2eabf5f4-ee7d-4c35-88bd-82fd5da1b912 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:32.403935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 592
 
11.1%
4 410
 
7.7%
b 356
 
6.7%
5 316
 
5.9%
a 312
 
5.9%
c 306
 
5.7%
8 304
 
5.7%
9 300
 
5.6%
0 292
 
5.5%
1 290
 
5.4%
Other values (7) 1850
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 410
 
7.7%
b 356
 
6.7%
5 316
 
5.9%
a 312
 
5.9%
c 306
 
5.7%
8 304
 
5.7%
9 300
 
5.6%
0 292
 
5.5%
1 290
 
5.4%
Other values (7) 1850
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 410
 
7.7%
b 356
 
6.7%
5 316
 
5.9%
a 312
 
5.9%
c 306
 
5.7%
8 304
 
5.7%
9 300
 
5.6%
0 292
 
5.5%
1 290
 
5.4%
Other values (7) 1850
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 410
 
7.7%
b 356
 
6.7%
5 316
 
5.9%
a 312
 
5.9%
c 306
 
5.7%
8 304
 
5.7%
9 300
 
5.6%
0 292
 
5.5%
1 290
 
5.4%
Other values (7) 1850
34.7%

primary_site
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Other and ill-defined digestive organs
148 

Length

Max length38
Median length38
Mean length38
Min length38

Characters and Unicode

Total characters5624
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther and ill-defined digestive organs
2nd rowOther and ill-defined digestive organs
3rd rowOther and ill-defined digestive organs
4th rowOther and ill-defined digestive organs
5th rowOther and ill-defined digestive organs

Common Values

ValueCountFrequency (%)
Other and ill-defined digestive organs 148
100.0%

Length

2025-07-21T11:07:32.440853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:32.463114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
other 148
20.0%
and 148
20.0%
ill-defined 148
20.0%
digestive 148
20.0%
organs 148
20.0%

Most occurring characters

ValueCountFrequency (%)
e 740
13.2%
d 592
10.5%
592
10.5%
i 592
10.5%
n 444
 
7.9%
g 296
 
5.3%
r 296
 
5.3%
a 296
 
5.3%
t 296
 
5.3%
l 296
 
5.3%
Other values (7) 1184
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 740
13.2%
d 592
10.5%
592
10.5%
i 592
10.5%
n 444
 
7.9%
g 296
 
5.3%
r 296
 
5.3%
a 296
 
5.3%
t 296
 
5.3%
l 296
 
5.3%
Other values (7) 1184
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 740
13.2%
d 592
10.5%
592
10.5%
i 592
10.5%
n 444
 
7.9%
g 296
 
5.3%
r 296
 
5.3%
a 296
 
5.3%
t 296
 
5.3%
l 296
 
5.3%
Other values (7) 1184
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 740
13.2%
d 592
10.5%
592
10.5%
i 592
10.5%
n 444
 
7.9%
g 296
 
5.3%
r 296
 
5.3%
a 296
 
5.3%
t 296
 
5.3%
l 296
 
5.3%
Other values (7) 1184
21.1%

disease_type
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Complex Mixed and Stromal Neoplasms
148 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters5180
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplex Mixed and Stromal Neoplasms
2nd rowComplex Mixed and Stromal Neoplasms
3rd rowComplex Mixed and Stromal Neoplasms
4th rowComplex Mixed and Stromal Neoplasms
5th rowComplex Mixed and Stromal Neoplasms

Common Values

ValueCountFrequency (%)
Complex Mixed and Stromal Neoplasms 148
100.0%

Length

2025-07-21T11:07:32.485637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:32.503483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
complex 148
20.0%
mixed 148
20.0%
and 148
20.0%
stromal 148
20.0%
neoplasms 148
20.0%

Most occurring characters

ValueCountFrequency (%)
592
11.4%
m 444
 
8.6%
l 444
 
8.6%
e 444
 
8.6%
o 444
 
8.6%
a 444
 
8.6%
s 296
 
5.7%
p 296
 
5.7%
x 296
 
5.7%
d 296
 
5.7%
Other values (8) 1184
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
592
11.4%
m 444
 
8.6%
l 444
 
8.6%
e 444
 
8.6%
o 444
 
8.6%
a 444
 
8.6%
s 296
 
5.7%
p 296
 
5.7%
x 296
 
5.7%
d 296
 
5.7%
Other values (8) 1184
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
592
11.4%
m 444
 
8.6%
l 444
 
8.6%
e 444
 
8.6%
o 444
 
8.6%
a 444
 
8.6%
s 296
 
5.7%
p 296
 
5.7%
x 296
 
5.7%
d 296
 
5.7%
Other values (8) 1184
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
592
11.4%
m 444
 
8.6%
l 444
 
8.6%
e 444
 
8.6%
o 444
 
8.6%
a 444
 
8.6%
s 296
 
5.7%
p 296
 
5.7%
x 296
 
5.7%
d 296
 
5.7%
Other values (8) 1184
22.9%

updated_datetime
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2018-10-25T11:34:27.425461-05:00
148 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters4736
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-10-25T11:34:27.425461-05:00
2nd row2018-10-25T11:34:27.425461-05:00
3rd row2018-10-25T11:34:27.425461-05:00
4th row2018-10-25T11:34:27.425461-05:00
5th row2018-10-25T11:34:27.425461-05:00

Common Values

ValueCountFrequency (%)
2018-10-25T11:34:27.425461-05:00 148
100.0%

Length

2025-07-21T11:07:32.525932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:32.542595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2018-10-25t11:34:27.425461-05:00 148
100.0%

Most occurring characters

ValueCountFrequency (%)
0 740
15.6%
1 740
15.6%
2 592
12.5%
- 444
9.4%
5 444
9.4%
: 444
9.4%
4 444
9.4%
8 148
 
3.1%
T 148
 
3.1%
3 148
 
3.1%
Other values (3) 444
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 740
15.6%
1 740
15.6%
2 592
12.5%
- 444
9.4%
5 444
9.4%
: 444
9.4%
4 444
9.4%
8 148
 
3.1%
T 148
 
3.1%
3 148
 
3.1%
Other values (3) 444
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 740
15.6%
1 740
15.6%
2 592
12.5%
- 444
9.4%
5 444
9.4%
: 444
9.4%
4 444
9.4%
8 148
 
3.1%
T 148
 
3.1%
3 148
 
3.1%
Other values (3) 444
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 740
15.6%
1 740
15.6%
2 592
12.5%
- 444
9.4%
5 444
9.4%
: 444
9.4%
4 444
9.4%
8 148
 
3.1%
T 148
 
3.1%
3 148
 
3.1%
Other values (3) 444
9.4%

submitter_id_clinical
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:32.638315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3648649
Min length5

Characters and Unicode

Total characters942
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD6786
2nd rowAD6786
3rd rowAD16503
4th rowAD16503
5th rowAD15350
ValueCountFrequency (%)
ad6786 2
 
1.4%
ad12493 2
 
1.4%
ad15243 2
 
1.4%
ad739 2
 
1.4%
ad4181 2
 
1.4%
ad8732 2
 
1.4%
ad5136 2
 
1.4%
ad13968 2
 
1.4%
ad5662 2
 
1.4%
ad11565 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:32.775865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

created_datetime
Date

Distinct67
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2017-06-01 08:32:27.034873-05:00
Maximum2017-06-01 09:49:51.660438-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:32.816032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:32.855960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

project__project_id_clinical
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
FM-AD
148 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFM-AD
2nd rowFM-AD
3rd rowFM-AD
4th rowFM-AD
5th rowFM-AD

Common Values

ValueCountFrequency (%)
FM-AD 148
100.0%

Length

2025-07-21T11:07:32.888749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:32.905578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fm-ad 148
100.0%

Most occurring characters

ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

demographic__demographic_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:32.994295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters5328
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa97f73aa-6885-4c60-ac07-1af161177361
2nd rowa97f73aa-6885-4c60-ac07-1af161177361
3rd row54b9ae57-48ea-4376-81b5-840164116d33
4th row54b9ae57-48ea-4376-81b5-840164116d33
5th row79c8986d-3158-4f2b-93ed-ede1de151cd2
ValueCountFrequency (%)
a97f73aa-6885-4c60-ac07-1af161177361 2
 
1.4%
79ed47ec-1f2d-4625-9780-38785ec0af1c 2
 
1.4%
b8b726d9-0e5d-4fec-be03-7fb446154a55 2
 
1.4%
2dbac075-40eb-4a03-b98e-34aded6819bc 2
 
1.4%
fd2d426a-ccf1-411f-a70f-19bab16dc4cc 2
 
1.4%
4045f4a1-49a6-45f0-92fd-74a5f2b9c8bf 2
 
1.4%
15baffe0-f369-4c5d-b84a-0b6881d530b7 2
 
1.4%
8ea68e56-d1f9-4bd0-bebd-362e38c0807d 2
 
1.4%
198b0234-266d-47d6-9db1-0c9afd837c61 2
 
1.4%
9e9fc1e6-14db-4a03-8fcc-0ce884b70514 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:33.125486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 592
 
11.1%
4 422
 
7.9%
b 326
 
6.1%
a 320
 
6.0%
d 308
 
5.8%
2 304
 
5.7%
9 304
 
5.7%
8 290
 
5.4%
0 282
 
5.3%
c 280
 
5.3%
Other values (7) 1900
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 422
 
7.9%
b 326
 
6.1%
a 320
 
6.0%
d 308
 
5.8%
2 304
 
5.7%
9 304
 
5.7%
8 290
 
5.4%
0 282
 
5.3%
c 280
 
5.3%
Other values (7) 1900
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 422
 
7.9%
b 326
 
6.1%
a 320
 
6.0%
d 308
 
5.8%
2 304
 
5.7%
9 304
 
5.7%
8 290
 
5.4%
0 282
 
5.3%
c 280
 
5.3%
Other values (7) 1900
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 422
 
7.9%
b 326
 
6.1%
a 320
 
6.0%
d 308
 
5.8%
2 304
 
5.7%
9 304
 
5.7%
8 290
 
5.4%
0 282
 
5.3%
c 280
 
5.3%
Other values (7) 1900
35.7%

demographic__gender
Categorical

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
male
78 
female
70 

Length

Max length6
Median length4
Mean length4.9459459
Min length4

Characters and Unicode

Total characters732
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
male 78
52.7%
female 70
47.3%

Length

2025-07-21T11:07:33.162382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:33.181049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 78
52.7%
female 70
47.3%

Most occurring characters

ValueCountFrequency (%)
e 218
29.8%
m 148
20.2%
a 148
20.2%
l 148
20.2%
f 70
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 218
29.8%
m 148
20.2%
a 148
20.2%
l 148
20.2%
f 70
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 218
29.8%
m 148
20.2%
a 148
20.2%
l 148
20.2%
f 70
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 218
29.8%
m 148
20.2%
a 148
20.2%
l 148
20.2%
f 70
 
9.6%

demographic__updated_datetime
Categorical

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2019-04-26T16:29:13.804345-05:00
112 
2019-04-26T16:28:42.290058-05:00
34 
2019-04-26T16:29:44.074326-05:00
 
2

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters4736
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-04-26T16:29:13.804345-05:00
2nd row2019-04-26T16:29:13.804345-05:00
3rd row2019-04-26T16:29:13.804345-05:00
4th row2019-04-26T16:29:13.804345-05:00
5th row2019-04-26T16:29:13.804345-05:00

Common Values

ValueCountFrequency (%)
2019-04-26T16:29:13.804345-05:00 112
75.7%
2019-04-26T16:28:42.290058-05:00 34
 
23.0%
2019-04-26T16:29:44.074326-05:00 2
 
1.4%

Length

2025-07-21T11:07:33.205232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:33.224808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2019-04-26t16:29:13.804345-05:00 112
75.7%
2019-04-26t16:28:42.290058-05:00 34
 
23.0%
2019-04-26t16:29:44.074326-05:00 2
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 922
19.5%
2 514
10.9%
- 444
9.4%
: 444
9.4%
4 412
8.7%
1 408
8.6%
6 298
 
6.3%
9 296
 
6.2%
5 294
 
6.2%
3 226
 
4.8%
Other values (4) 478
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 922
19.5%
2 514
10.9%
- 444
9.4%
: 444
9.4%
4 412
8.7%
1 408
8.6%
6 298
 
6.3%
9 296
 
6.2%
5 294
 
6.2%
3 226
 
4.8%
Other values (4) 478
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 922
19.5%
2 514
10.9%
- 444
9.4%
: 444
9.4%
4 412
8.7%
1 408
8.6%
6 298
 
6.3%
9 296
 
6.2%
5 294
 
6.2%
3 226
 
4.8%
Other values (4) 478
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 922
19.5%
2 514
10.9%
- 444
9.4%
: 444
9.4%
4 412
8.7%
1 408
8.6%
6 298
 
6.3%
9 296
 
6.2%
5 294
 
6.2%
3 226
 
4.8%
Other values (4) 478
10.1%

demographic__submitter_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:33.306281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length18.364865
Min length17

Characters and Unicode

Total characters2718
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD6786_demographic
2nd rowAD6786_demographic
3rd rowAD16503_demographic
4th rowAD16503_demographic
5th rowAD15350_demographic
ValueCountFrequency (%)
ad6786_demographic 2
 
1.4%
ad12493_demographic 2
 
1.4%
ad15243_demographic 2
 
1.4%
ad739_demographic 2
 
1.4%
ad4181_demographic 2
 
1.4%
ad8732_demographic 2
 
1.4%
ad5136_demographic 2
 
1.4%
ad13968_demographic 2
 
1.4%
ad5662_demographic 2
 
1.4%
ad11565_demographic 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:33.423313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 148
 
5.4%
o 148
 
5.4%
c 148
 
5.4%
i 148
 
5.4%
h 148
 
5.4%
p 148
 
5.4%
D 148
 
5.4%
r 148
 
5.4%
g 148
 
5.4%
a 148
 
5.4%
Other values (14) 1238
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 148
 
5.4%
o 148
 
5.4%
c 148
 
5.4%
i 148
 
5.4%
h 148
 
5.4%
p 148
 
5.4%
D 148
 
5.4%
r 148
 
5.4%
g 148
 
5.4%
a 148
 
5.4%
Other values (14) 1238
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 148
 
5.4%
o 148
 
5.4%
c 148
 
5.4%
i 148
 
5.4%
h 148
 
5.4%
p 148
 
5.4%
D 148
 
5.4%
r 148
 
5.4%
g 148
 
5.4%
a 148
 
5.4%
Other values (14) 1238
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 148
 
5.4%
o 148
 
5.4%
c 148
 
5.4%
i 148
 
5.4%
h 148
 
5.4%
p 148
 
5.4%
D 148
 
5.4%
r 148
 
5.4%
g 148
 
5.4%
a 148
 
5.4%
Other values (14) 1238
45.5%

demographic__created_datetime
Date

Distinct67
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2017-06-19 11:22:10.745969-05:00
Maximum2017-06-19 11:51:09.974481-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:33.460502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:33.567944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

diagnoses__morphology
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
8936/1
148 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters888
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8936/1
2nd row8936/1
3rd row8936/1
4th row8936/1
5th row8936/1

Common Values

ValueCountFrequency (%)
8936/1 148
100.0%

Length

2025-07-21T11:07:33.603932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:33.620019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8936/1 148
100.0%

Most occurring characters

ValueCountFrequency (%)
8 148
16.7%
9 148
16.7%
3 148
16.7%
6 148
16.7%
/ 148
16.7%
1 148
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 148
16.7%
9 148
16.7%
3 148
16.7%
6 148
16.7%
/ 148
16.7%
1 148
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 148
16.7%
9 148
16.7%
3 148
16.7%
6 148
16.7%
/ 148
16.7%
1 148
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 148
16.7%
9 148
16.7%
3 148
16.7%
6 148
16.7%
/ 148
16.7%
1 148
16.7%

diagnoses__submitter_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:33.697183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length16.364865
Min length15

Characters and Unicode

Total characters2422
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD6786_diagnosis
2nd rowAD6786_diagnosis
3rd rowAD16503_diagnosis
4th rowAD16503_diagnosis
5th rowAD15350_diagnosis
ValueCountFrequency (%)
ad6786_diagnosis 2
 
1.4%
ad12493_diagnosis 2
 
1.4%
ad15243_diagnosis 2
 
1.4%
ad739_diagnosis 2
 
1.4%
ad4181_diagnosis 2
 
1.4%
ad8732_diagnosis 2
 
1.4%
ad5136_diagnosis 2
 
1.4%
ad13968_diagnosis 2
 
1.4%
ad5662_diagnosis 2
 
1.4%
ad11565_diagnosis 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:33.818117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 296
12.2%
s 296
12.2%
A 148
 
6.1%
o 148
 
6.1%
D 148
 
6.1%
g 148
 
6.1%
a 148
 
6.1%
n 148
 
6.1%
d 148
 
6.1%
_ 148
 
6.1%
Other values (10) 646
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 296
12.2%
s 296
12.2%
A 148
 
6.1%
o 148
 
6.1%
D 148
 
6.1%
g 148
 
6.1%
a 148
 
6.1%
n 148
 
6.1%
d 148
 
6.1%
_ 148
 
6.1%
Other values (10) 646
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 296
12.2%
s 296
12.2%
A 148
 
6.1%
o 148
 
6.1%
D 148
 
6.1%
g 148
 
6.1%
a 148
 
6.1%
n 148
 
6.1%
d 148
 
6.1%
_ 148
 
6.1%
Other values (10) 646
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 296
12.2%
s 296
12.2%
A 148
 
6.1%
o 148
 
6.1%
D 148
 
6.1%
g 148
 
6.1%
a 148
 
6.1%
n 148
 
6.1%
d 148
 
6.1%
_ 148
 
6.1%
Other values (10) 646
26.7%

diagnoses__created_datetime
Date

Distinct67
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2017-06-16 15:38:46.182862-05:00
Maximum2017-06-19 09:33:23.901600-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:33.855751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:33.895088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

diagnoses__age_at_diagnosis
Real number (ℝ)

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20664.459
Minimum9080
Maximum29608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:33.932969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9080
5-th percentile11198.6
Q118010
median21223.5
Q324399
95-th percentile27393.6
Maximum29608
Range20528
Interquartile range (IQR)6389

Descriptive statistics

Standard deviation4659.8777
Coefficient of variation (CV)0.22550204
Kurtosis-0.1776276
Mean20664.459
Median Absolute Deviation (MAD)3194.5
Skewness-0.49797962
Sum3058340
Variance21714460
MonotonicityNot monotonic
2025-07-21T11:07:33.970054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25495 2
 
1.4%
21406 2
 
1.4%
19086 2
 
1.4%
14603 2
 
1.4%
23751 2
 
1.4%
21912 2
 
1.4%
25145 2
 
1.4%
14472 2
 
1.4%
20232 2
 
1.4%
29608 2
 
1.4%
Other values (64) 128
86.5%
ValueCountFrequency (%)
9080 2
1.4%
9681 2
1.4%
10087 2
1.4%
11193 2
1.4%
11209 2
1.4%
13507 2
1.4%
14472 2
1.4%
14603 2
1.4%
15101 2
1.4%
15548 2
1.4%
ValueCountFrequency (%)
29608 2
1.4%
28710 2
1.4%
27450 2
1.4%
27423 2
1.4%
27339 2
1.4%
26307 2
1.4%
26016 2
1.4%
25914 2
1.4%
25711 2
1.4%
25660 2
1.4%

diagnoses__primary_diagnosis
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Gastrointestinal stromal tumor, NOS
148 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters5180
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGastrointestinal stromal tumor, NOS
2nd rowGastrointestinal stromal tumor, NOS
3rd rowGastrointestinal stromal tumor, NOS
4th rowGastrointestinal stromal tumor, NOS
5th rowGastrointestinal stromal tumor, NOS

Common Values

ValueCountFrequency (%)
Gastrointestinal stromal tumor, NOS 148
100.0%

Length

2025-07-21T11:07:34.002735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:34.019354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gastrointestinal 148
25.0%
stromal 148
25.0%
tumor 148
25.0%
nos 148
25.0%

Most occurring characters

ValueCountFrequency (%)
t 740
14.3%
s 444
8.6%
r 444
8.6%
o 444
8.6%
a 444
8.6%
444
8.6%
i 296
 
5.7%
n 296
 
5.7%
l 296
 
5.7%
m 296
 
5.7%
Other values (7) 1036
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 740
14.3%
s 444
8.6%
r 444
8.6%
o 444
8.6%
a 444
8.6%
444
8.6%
i 296
 
5.7%
n 296
 
5.7%
l 296
 
5.7%
m 296
 
5.7%
Other values (7) 1036
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 740
14.3%
s 444
8.6%
r 444
8.6%
o 444
8.6%
a 444
8.6%
444
8.6%
i 296
 
5.7%
n 296
 
5.7%
l 296
 
5.7%
m 296
 
5.7%
Other values (7) 1036
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 740
14.3%
s 444
8.6%
r 444
8.6%
o 444
8.6%
a 444
8.6%
444
8.6%
i 296
 
5.7%
n 296
 
5.7%
l 296
 
5.7%
m 296
 
5.7%
Other values (7) 1036
20.0%

diagnoses__classification_of_tumor
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
metastasis
144 
Unknown
 
4

Length

Max length10
Median length10
Mean length9.9189189
Min length7

Characters and Unicode

Total characters1468
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmetastasis
2nd rowmetastasis
3rd rowmetastasis
4th rowmetastasis
5th rowmetastasis

Common Values

ValueCountFrequency (%)
metastasis 144
97.3%
Unknown 4
 
2.7%

Length

2025-07-21T11:07:34.042801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:34.062168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metastasis 144
97.3%
unknown 4
 
2.7%

Most occurring characters

ValueCountFrequency (%)
s 432
29.4%
t 288
19.6%
a 288
19.6%
m 144
 
9.8%
e 144
 
9.8%
i 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
o 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 432
29.4%
t 288
19.6%
a 288
19.6%
m 144
 
9.8%
e 144
 
9.8%
i 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
o 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 432
29.4%
t 288
19.6%
a 288
19.6%
m 144
 
9.8%
e 144
 
9.8%
i 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
o 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 432
29.4%
t 288
19.6%
a 288
19.6%
m 144
 
9.8%
e 144
 
9.8%
i 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
o 4
 
0.3%

diagnoses__updated_datetime
Date

Distinct59
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2019-07-10 12:42:15.422992-05:00
Maximum2019-07-10 13:51:04.916029-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:34.087679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:34.125065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

diagnoses__diagnosis_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:34.227385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters5328
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row58666d98-0fa4-4dba-9bd5-7a8ef139ea42
2nd row58666d98-0fa4-4dba-9bd5-7a8ef139ea42
3rd rowf4f536aa-7abe-48c2-8794-46a77d9e6e29
4th rowf4f536aa-7abe-48c2-8794-46a77d9e6e29
5th rowc15ee7ef-99af-4fcf-9329-408ddffc99c3
ValueCountFrequency (%)
58666d98-0fa4-4dba-9bd5-7a8ef139ea42 2
 
1.4%
a51db4b0-e727-4580-b72c-8f6663d51438 2
 
1.4%
ff9c4ba7-1198-4466-9489-a3e15a09d77c 2
 
1.4%
541fad7d-bf7a-4366-836a-11245198496e 2
 
1.4%
89600076-49a1-4f60-af87-77c0985807a4 2
 
1.4%
183bb9d2-6ae7-4efa-95a1-7c35d7234299 2
 
1.4%
ba14960c-6635-480b-a71f-8b08decd7100 2
 
1.4%
e4ed23f8-712d-4da3-b887-6ba7489499b3 2
 
1.4%
8876fc7a-cfbf-4272-a135-1f941f385a77 2
 
1.4%
a0e52548-c30b-4857-a4c6-0e1a893e5090 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:34.360583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 592
 
11.1%
4 436
 
8.2%
a 344
 
6.5%
2 328
 
6.2%
9 320
 
6.0%
6 318
 
6.0%
8 314
 
5.9%
f 298
 
5.6%
c 292
 
5.5%
b 280
 
5.3%
Other values (7) 1806
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 436
 
8.2%
a 344
 
6.5%
2 328
 
6.2%
9 320
 
6.0%
6 318
 
6.0%
8 314
 
5.9%
f 298
 
5.6%
c 292
 
5.5%
b 280
 
5.3%
Other values (7) 1806
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 436
 
8.2%
a 344
 
6.5%
2 328
 
6.2%
9 320
 
6.0%
6 318
 
6.0%
8 314
 
5.9%
f 298
 
5.6%
c 292
 
5.5%
b 280
 
5.3%
Other values (7) 1806
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 436
 
8.2%
a 344
 
6.5%
2 328
 
6.2%
9 320
 
6.0%
6 318
 
6.0%
8 314
 
5.9%
f 298
 
5.6%
c 292
 
5.5%
b 280
 
5.3%
Other values (7) 1806
33.9%

diagnoses__site_of_resection_or_biopsy
Categorical

High correlation 

Distinct17
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Small intestine, NOS
38 
Stomach, NOS
36 
Abdomen, NOS
16 
Connective, subcutaneous and other soft tissues, NOS
12 
Colon, NOS
8 
Other values (12)
38 

Length

Max length57
Median length52
Mean length18.216216
Min length5

Characters and Unicode

Total characters2696
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall intestine, NOS
2nd rowSmall intestine, NOS
3rd rowColon, NOS
4th rowColon, NOS
5th rowConnective, subcutaneous and other soft tissues, NOS

Common Values

ValueCountFrequency (%)
Small intestine, NOS 38
25.7%
Stomach, NOS 36
24.3%
Abdomen, NOS 16
10.8%
Connective, subcutaneous and other soft tissues, NOS 12
 
8.1%
Colon, NOS 8
 
5.4%
Specified parts of peritoneum 8
 
5.4%
Liver 8
 
5.4%
Not Reported 4
 
2.7%
Peritoneum, NOS 2
 
1.4%
Lymph node, NOS 2
 
1.4%
Other values (7) 14
 
9.5%

Length

2025-07-21T11:07:34.398404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nos 122
29.6%
small 38
 
9.2%
intestine 38
 
9.2%
stomach 36
 
8.7%
abdomen 16
 
3.9%
connective 14
 
3.4%
subcutaneous 14
 
3.4%
and 14
 
3.4%
other 14
 
3.4%
soft 14
 
3.4%
Other values (18) 92
22.3%

Most occurring characters

ValueCountFrequency (%)
264
 
9.8%
e 224
 
8.3%
t 210
 
7.8%
S 204
 
7.6%
n 174
 
6.5%
o 160
 
5.9%
i 142
 
5.3%
, 136
 
5.0%
s 134
 
5.0%
N 126
 
4.7%
Other values (22) 922
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
264
 
9.8%
e 224
 
8.3%
t 210
 
7.8%
S 204
 
7.6%
n 174
 
6.5%
o 160
 
5.9%
i 142
 
5.3%
, 136
 
5.0%
s 134
 
5.0%
N 126
 
4.7%
Other values (22) 922
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
264
 
9.8%
e 224
 
8.3%
t 210
 
7.8%
S 204
 
7.6%
n 174
 
6.5%
o 160
 
5.9%
i 142
 
5.3%
, 136
 
5.0%
s 134
 
5.0%
N 126
 
4.7%
Other values (22) 922
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
264
 
9.8%
e 224
 
8.3%
t 210
 
7.8%
S 204
 
7.6%
n 174
 
6.5%
o 160
 
5.9%
i 142
 
5.3%
, 136
 
5.0%
s 134
 
5.0%
N 126
 
4.7%
Other values (22) 922
34.2%

submitter_id_bio
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:34.496932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3648649
Min length5

Characters and Unicode

Total characters942
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD6786
2nd rowAD6786
3rd rowAD16503
4th rowAD16503
5th rowAD15350
ValueCountFrequency (%)
ad6786 2
 
1.4%
ad12493 2
 
1.4%
ad15243 2
 
1.4%
ad739 2
 
1.4%
ad4181 2
 
1.4%
ad8732 2
 
1.4%
ad5136 2
 
1.4%
ad13968 2
 
1.4%
ad5662 2
 
1.4%
ad11565 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:34.630781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 148
15.7%
D 148
15.7%
1 126
13.4%
3 74
7.9%
7 68
7.2%
5 64
6.8%
4 64
6.8%
8 62
6.6%
6 52
 
5.5%
0 48
 
5.1%
Other values (2) 88
9.3%

project__project_id_bio
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
FM-AD
148 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFM-AD
2nd rowFM-AD
3rd rowFM-AD
4th rowFM-AD
5th rowFM-AD

Common Values

ValueCountFrequency (%)
FM-AD 148
100.0%

Length

2025-07-21T11:07:34.665422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:34.681865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fm-ad 148
100.0%

Most occurring characters

ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 148
20.0%
M 148
20.0%
- 148
20.0%
A 148
20.0%
D 148
20.0%

samples__tumor_descriptor
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Metastatic
144 
Unknown
 
4

Length

Max length10
Median length10
Mean length9.9189189
Min length7

Characters and Unicode

Total characters1468
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetastatic
2nd rowMetastatic
3rd rowMetastatic
4th rowMetastatic
5th rowMetastatic

Common Values

ValueCountFrequency (%)
Metastatic 144
97.3%
Unknown 4
 
2.7%

Length

2025-07-21T11:07:34.704820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:34.723468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metastatic 144
97.3%
unknown 4
 
2.7%

Most occurring characters

ValueCountFrequency (%)
t 432
29.4%
a 288
19.6%
M 144
 
9.8%
e 144
 
9.8%
s 144
 
9.8%
i 144
 
9.8%
c 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
Other values (2) 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 432
29.4%
a 288
19.6%
M 144
 
9.8%
e 144
 
9.8%
s 144
 
9.8%
i 144
 
9.8%
c 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
Other values (2) 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 432
29.4%
a 288
19.6%
M 144
 
9.8%
e 144
 
9.8%
s 144
 
9.8%
i 144
 
9.8%
c 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
Other values (2) 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 432
29.4%
a 288
19.6%
M 144
 
9.8%
e 144
 
9.8%
s 144
 
9.8%
i 144
 
9.8%
c 144
 
9.8%
n 12
 
0.8%
U 4
 
0.3%
k 4
 
0.3%
Other values (2) 8
 
0.5%

samples__updated_datetime
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-10-13T14:28:47.406440-05:00
148 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters4736
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-13T14:28:47.406440-05:00
2nd row2023-10-13T14:28:47.406440-05:00
3rd row2023-10-13T14:28:47.406440-05:00
4th row2023-10-13T14:28:47.406440-05:00
5th row2023-10-13T14:28:47.406440-05:00

Common Values

ValueCountFrequency (%)
2023-10-13T14:28:47.406440-05:00 148
100.0%

Length

2025-07-21T11:07:34.745878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:34.763451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-13t14:28:47.406440-05:00 148
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1036
21.9%
4 740
15.6%
2 444
9.4%
- 444
9.4%
1 444
9.4%
: 444
9.4%
3 296
 
6.2%
T 148
 
3.1%
8 148
 
3.1%
7 148
 
3.1%
Other values (3) 444
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1036
21.9%
4 740
15.6%
2 444
9.4%
- 444
9.4%
1 444
9.4%
: 444
9.4%
3 296
 
6.2%
T 148
 
3.1%
8 148
 
3.1%
7 148
 
3.1%
Other values (3) 444
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1036
21.9%
4 740
15.6%
2 444
9.4%
- 444
9.4%
1 444
9.4%
: 444
9.4%
3 296
 
6.2%
T 148
 
3.1%
8 148
 
3.1%
7 148
 
3.1%
Other values (3) 444
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1036
21.9%
4 740
15.6%
2 444
9.4%
- 444
9.4%
1 444
9.4%
: 444
9.4%
3 296
 
6.2%
T 148
 
3.1%
8 148
 
3.1%
7 148
 
3.1%
Other values (3) 444
9.4%

samples__sample_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:34.853930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters5328
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03c0bf16-1381-4969-bb76-01cdfb0fba35
2nd row03c0bf16-1381-4969-bb76-01cdfb0fba35
3rd rowb3b8e1b3-8bea-4223-8fd1-34fc55127207
4th rowb3b8e1b3-8bea-4223-8fd1-34fc55127207
5th row337f28e1-0c6f-4f4b-b594-13192df2dfbb
ValueCountFrequency (%)
03c0bf16-1381-4969-bb76-01cdfb0fba35 2
 
1.4%
3b9bd776-1827-4a5a-ba23-faa22d27c316 2
 
1.4%
be3d7a06-4ec5-4b44-90d1-b7b2552a9831 2
 
1.4%
a9684a01-d037-4444-929e-acd6bfa2a8cf 2
 
1.4%
bb080e29-55e9-4cd9-91bb-94c854c6a3ec 2
 
1.4%
02638c90-c4c7-475c-b62b-f34a3acefb9b 2
 
1.4%
f0413178-d7fe-41f2-bd9e-2835f91649c3 2
 
1.4%
1e3f601f-a11b-4fd0-b0cc-70427f2d34de 2
 
1.4%
fc9aecb5-f3fa-4dd4-8e09-0b12ee9c9132 2
 
1.4%
b7483bf6-ab39-4a94-b4a7-76734a7e687e 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:35.006217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 592
 
11.1%
4 468
 
8.8%
b 334
 
6.3%
2 330
 
6.2%
9 322
 
6.0%
a 318
 
6.0%
3 298
 
5.6%
f 284
 
5.3%
1 284
 
5.3%
6 282
 
5.3%
Other values (7) 1816
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 468
 
8.8%
b 334
 
6.3%
2 330
 
6.2%
9 322
 
6.0%
a 318
 
6.0%
3 298
 
5.6%
f 284
 
5.3%
1 284
 
5.3%
6 282
 
5.3%
Other values (7) 1816
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 468
 
8.8%
b 334
 
6.3%
2 330
 
6.2%
9 322
 
6.0%
a 318
 
6.0%
3 298
 
5.6%
f 284
 
5.3%
1 284
 
5.3%
6 282
 
5.3%
Other values (7) 1816
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
4 468
 
8.8%
b 334
 
6.3%
2 330
 
6.2%
9 322
 
6.0%
a 318
 
6.0%
3 298
 
5.6%
f 284
 
5.3%
1 284
 
5.3%
6 282
 
5.3%
Other values (7) 1816
34.1%

samples__submitter_id
Text

Distinct74
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:35.100136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length13
Mean length13.364865
Min length12

Characters and Unicode

Total characters1978
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD6786_sample
2nd rowAD6786_sample
3rd rowAD16503_sample
4th rowAD16503_sample
5th rowAD15350_sample
ValueCountFrequency (%)
ad6786_sample 2
 
1.4%
ad12493_sample 2
 
1.4%
ad15243_sample 2
 
1.4%
ad739_sample 2
 
1.4%
ad4181_sample 2
 
1.4%
ad8732_sample 2
 
1.4%
ad5136_sample 2
 
1.4%
ad13968_sample 2
 
1.4%
ad5662_sample 2
 
1.4%
ad11565_sample 2
 
1.4%
Other values (64) 128
86.5%
2025-07-21T11:07:35.225656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 148
 
7.5%
a 148
 
7.5%
e 148
 
7.5%
l 148
 
7.5%
D 148
 
7.5%
m 148
 
7.5%
p 148
 
7.5%
s 148
 
7.5%
_ 148
 
7.5%
1 126
 
6.4%
Other values (9) 520
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 148
 
7.5%
a 148
 
7.5%
e 148
 
7.5%
l 148
 
7.5%
D 148
 
7.5%
m 148
 
7.5%
p 148
 
7.5%
s 148
 
7.5%
_ 148
 
7.5%
1 126
 
6.4%
Other values (9) 520
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 148
 
7.5%
a 148
 
7.5%
e 148
 
7.5%
l 148
 
7.5%
D 148
 
7.5%
m 148
 
7.5%
p 148
 
7.5%
s 148
 
7.5%
_ 148
 
7.5%
1 126
 
6.4%
Other values (9) 520
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 148
 
7.5%
a 148
 
7.5%
e 148
 
7.5%
l 148
 
7.5%
D 148
 
7.5%
m 148
 
7.5%
p 148
 
7.5%
s 148
 
7.5%
_ 148
 
7.5%
1 126
 
6.4%
Other values (9) 520
26.3%

samples__sample_type
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Metastatic
144 
Tumor
 
4

Length

Max length10
Median length10
Mean length9.8648649
Min length5

Characters and Unicode

Total characters1460
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetastatic
2nd rowMetastatic
3rd rowMetastatic
4th rowMetastatic
5th rowMetastatic

Common Values

ValueCountFrequency (%)
Metastatic 144
97.3%
Tumor 4
 
2.7%

Length

2025-07-21T11:07:35.260424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:35.279127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metastatic 144
97.3%
tumor 4
 
2.7%

Most occurring characters

ValueCountFrequency (%)
t 432
29.6%
a 288
19.7%
M 144
 
9.9%
e 144
 
9.9%
s 144
 
9.9%
i 144
 
9.9%
c 144
 
9.9%
T 4
 
0.3%
u 4
 
0.3%
m 4
 
0.3%
Other values (2) 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 432
29.6%
a 288
19.7%
M 144
 
9.9%
e 144
 
9.9%
s 144
 
9.9%
i 144
 
9.9%
c 144
 
9.9%
T 4
 
0.3%
u 4
 
0.3%
m 4
 
0.3%
Other values (2) 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 432
29.6%
a 288
19.7%
M 144
 
9.9%
e 144
 
9.9%
s 144
 
9.9%
i 144
 
9.9%
c 144
 
9.9%
T 4
 
0.3%
u 4
 
0.3%
m 4
 
0.3%
Other values (2) 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 432
29.6%
a 288
19.7%
M 144
 
9.9%
e 144
 
9.9%
s 144
 
9.9%
i 144
 
9.9%
c 144
 
9.9%
T 4
 
0.3%
u 4
 
0.3%
m 4
 
0.3%
Other values (2) 8
 
0.5%

samples__created_datetime
Date

Distinct67
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2017-06-01 09:54:31.122378-05:00
Maximum2017-06-01 10:57:52.505988-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:35.305655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:35.346536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

samples__portions__portion_id
Text

Unique 

Distinct148
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:35.451043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters5328
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)100.0%

Sample

1st row4b15a185-52a7-5706-90fc-98993bb9adb4
2nd row02e4e948-f51b-592c-bed7-66e2857c3afe
3rd rowdff28d5e-7371-5553-8df8-3f0b7fccf162
4th row3a8c21ba-9e7c-5d06-8daf-e62666f4aa70
5th rowa43034e5-2fa4-51d2-9aa0-b9c1014bf634
ValueCountFrequency (%)
4b15a185-52a7-5706-90fc-98993bb9adb4 1
 
0.7%
2b15ef23-9483-5f47-9c7e-ad84214f5f18 1
 
0.7%
463e03ef-179d-5582-95a0-2969b76e129c 1
 
0.7%
dff28d5e-7371-5553-8df8-3f0b7fccf162 1
 
0.7%
3a8c21ba-9e7c-5d06-8daf-e62666f4aa70 1
 
0.7%
a43034e5-2fa4-51d2-9aa0-b9c1014bf634 1
 
0.7%
daf624d5-a5ed-5195-9760-10ec810f814a 1
 
0.7%
f55a1a11-e887-58bb-bb25-fc03699779e4 1
 
0.7%
47150fa5-f192-5028-acf5-ff06dcf1a886 1
 
0.7%
b9869132-7c25-5fad-ad22-0301ce6ae6f7 1
 
0.7%
Other values (138) 138
93.2%
2025-07-21T11:07:35.584600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 592
 
11.1%
5 417
 
7.8%
8 347
 
6.5%
b 318
 
6.0%
a 316
 
5.9%
9 310
 
5.8%
f 301
 
5.6%
0 297
 
5.6%
e 287
 
5.4%
7 282
 
5.3%
Other values (7) 1861
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
5 417
 
7.8%
8 347
 
6.5%
b 318
 
6.0%
a 316
 
5.9%
9 310
 
5.8%
f 301
 
5.6%
0 297
 
5.6%
e 287
 
5.4%
7 282
 
5.3%
Other values (7) 1861
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
5 417
 
7.8%
8 347
 
6.5%
b 318
 
6.0%
a 316
 
5.9%
9 310
 
5.8%
f 301
 
5.6%
0 297
 
5.6%
e 287
 
5.4%
7 282
 
5.3%
Other values (7) 1861
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 592
 
11.1%
5 417
 
7.8%
8 347
 
6.5%
b 318
 
6.0%
a 316
 
5.9%
9 310
 
5.8%
f 301
 
5.6%
0 297
 
5.6%
e 287
 
5.4%
7 282
 
5.3%
Other values (7) 1861
34.9%

samples__portions__analytes__analyte_id
Text

Missing 

Distinct74
Distinct (%)100.0%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2025-07-21T11:07:35.688122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters2664
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st row7fd74dee-043e-5f4c-9d89-72bc0fa50a69
2nd rowd4cd439c-5490-5930-a777-35a533313282
3rd rowa5c93ec7-4388-5ee0-818d-821f0339cfc9
4th rowe2b588e1-c10d-5a89-8514-b78317487523
5th row17fc7cb5-3183-58a8-8211-d9df5f848791
ValueCountFrequency (%)
d1c694dd-d9e8-5e2f-87de-cc421e06b2dd 1
 
1.4%
d58e8e77-52b4-5ee2-992c-39ccc1ee430c 1
 
1.4%
d4cd439c-5490-5930-a777-35a533313282 1
 
1.4%
a5c93ec7-4388-5ee0-818d-821f0339cfc9 1
 
1.4%
e2b588e1-c10d-5a89-8514-b78317487523 1
 
1.4%
17fc7cb5-3183-58a8-8211-d9df5f848791 1
 
1.4%
c463ae73-963f-5cf5-aba8-b6299fa2ee6f 1
 
1.4%
474b7093-0ece-5a5e-a4f7-ac0c92bbdc8d 1
 
1.4%
d0531c5e-cdd0-581a-be75-94630bcad8c0 1
 
1.4%
8f80cf3d-8e9e-5e59-895c-927ad19d6874 1
 
1.4%
Other values (64) 64
86.5%
2025-07-21T11:07:35.818463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 296
 
11.1%
5 200
 
7.5%
d 161
 
6.0%
9 161
 
6.0%
8 156
 
5.9%
a 153
 
5.7%
7 150
 
5.6%
3 150
 
5.6%
e 149
 
5.6%
4 146
 
5.5%
Other values (7) 942
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
5 200
 
7.5%
d 161
 
6.0%
9 161
 
6.0%
8 156
 
5.9%
a 153
 
5.7%
7 150
 
5.6%
3 150
 
5.6%
e 149
 
5.6%
4 146
 
5.5%
Other values (7) 942
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
5 200
 
7.5%
d 161
 
6.0%
9 161
 
6.0%
8 156
 
5.9%
a 153
 
5.7%
7 150
 
5.6%
3 150
 
5.6%
e 149
 
5.6%
4 146
 
5.5%
Other values (7) 942
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
5 200
 
7.5%
d 161
 
6.0%
9 161
 
6.0%
8 156
 
5.9%
a 153
 
5.7%
7 150
 
5.6%
3 150
 
5.6%
e 149
 
5.6%
4 146
 
5.5%
Other values (7) 942
35.4%

samples__portions__slides__updated_datetime
Categorical

Missing 

Distinct5
Distinct (%)6.8%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2018-08-23T19:39:28.814253-05:00
28 
2018-08-23T19:35:48.363089-05:00
20 
2018-08-23T19:31:53.272333-05:00
16 
2018-08-23T19:28:09.950838-05:00
7 
2018-08-23T19:43:04.434980-05:00
3 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2368
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-08-23T19:39:28.814253-05:00
2nd row2018-08-23T19:39:28.814253-05:00
3rd row2018-08-23T19:39:28.814253-05:00
4th row2018-08-23T19:39:28.814253-05:00
5th row2018-08-23T19:39:28.814253-05:00

Common Values

ValueCountFrequency (%)
2018-08-23T19:39:28.814253-05:00 28
 
18.9%
2018-08-23T19:35:48.363089-05:00 20
 
13.5%
2018-08-23T19:31:53.272333-05:00 16
 
10.8%
2018-08-23T19:28:09.950838-05:00 7
 
4.7%
2018-08-23T19:43:04.434980-05:00 3
 
2.0%
(Missing) 74
50.0%

Length

2025-07-21T11:07:35.853208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:35.875875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-23t19:39:28.814253-05:00 28
37.8%
2018-08-23t19:35:48.363089-05:00 20
27.0%
2018-08-23t19:31:53.272333-05:00 16
21.6%
2018-08-23t19:28:09.950838-05:00 7
 
9.5%
2018-08-23t19:43:04.434980-05:00 3
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 410
17.3%
3 283
12.0%
8 268
11.3%
2 243
10.3%
- 222
9.4%
: 222
9.4%
1 192
8.1%
5 145
 
6.1%
9 139
 
5.9%
T 74
 
3.1%
Other values (4) 170
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 410
17.3%
3 283
12.0%
8 268
11.3%
2 243
10.3%
- 222
9.4%
: 222
9.4%
1 192
8.1%
5 145
 
6.1%
9 139
 
5.9%
T 74
 
3.1%
Other values (4) 170
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 410
17.3%
3 283
12.0%
8 268
11.3%
2 243
10.3%
- 222
9.4%
: 222
9.4%
1 192
8.1%
5 145
 
6.1%
9 139
 
5.9%
T 74
 
3.1%
Other values (4) 170
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 410
17.3%
3 283
12.0%
8 268
11.3%
2 243
10.3%
- 222
9.4%
: 222
9.4%
1 192
8.1%
5 145
 
6.1%
9 139
 
5.9%
T 74
 
3.1%
Other values (4) 170
7.2%

samples__portions__slides__submitter_id
Text

Missing 

Distinct74
Distinct (%)100.0%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2025-07-21T11:07:35.975363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.364865
Min length11

Characters and Unicode

Total characters915
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st rowAD6786_slide
2nd rowAD16503_slide
3rd rowAD15350_slide
4th rowAD15243_slide
5th rowAD739_slide
ValueCountFrequency (%)
ad187_slide 1
 
1.4%
ad3300_slide 1
 
1.4%
ad15350_slide 1
 
1.4%
ad15243_slide 1
 
1.4%
ad739_slide 1
 
1.4%
ad4181_slide 1
 
1.4%
ad8732_slide 1
 
1.4%
ad5136_slide 1
 
1.4%
ad13968_slide 1
 
1.4%
ad5662_slide 1
 
1.4%
Other values (64) 64
86.5%
2025-07-21T11:07:36.103033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 74
 
8.1%
s 74
 
8.1%
e 74
 
8.1%
D 74
 
8.1%
i 74
 
8.1%
l 74
 
8.1%
d 74
 
8.1%
_ 74
 
8.1%
1 63
 
6.9%
3 37
 
4.0%
Other values (8) 223
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 74
 
8.1%
s 74
 
8.1%
e 74
 
8.1%
D 74
 
8.1%
i 74
 
8.1%
l 74
 
8.1%
d 74
 
8.1%
_ 74
 
8.1%
1 63
 
6.9%
3 37
 
4.0%
Other values (8) 223
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 74
 
8.1%
s 74
 
8.1%
e 74
 
8.1%
D 74
 
8.1%
i 74
 
8.1%
l 74
 
8.1%
d 74
 
8.1%
_ 74
 
8.1%
1 63
 
6.9%
3 37
 
4.0%
Other values (8) 223
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 74
 
8.1%
s 74
 
8.1%
e 74
 
8.1%
D 74
 
8.1%
i 74
 
8.1%
l 74
 
8.1%
d 74
 
8.1%
_ 74
 
8.1%
1 63
 
6.9%
3 37
 
4.0%
Other values (8) 223
24.4%

samples__portions__slides__slide_id
Text

Missing 

Distinct74
Distinct (%)100.0%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2025-07-21T11:07:36.206622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters2664
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st rowd2a40825-5b8b-424f-b0a6-c06022151a97
2nd rowd03e146d-ae65-4ac4-a5c3-86bd6cf3638c
3rd rowc9072edc-7ffc-46dc-b365-e1c8503bfadf
4th rowb4498a81-e7dc-4cd7-bac0-396120453e3a
5th rowc422c3ba-1848-4abf-9def-b470c11b3f6a
ValueCountFrequency (%)
9765debc-05bd-47d5-a682-eeebc38aea00 1
 
1.4%
2af96e03-d280-4d60-83ae-999a3e3b931b 1
 
1.4%
c9072edc-7ffc-46dc-b365-e1c8503bfadf 1
 
1.4%
b4498a81-e7dc-4cd7-bac0-396120453e3a 1
 
1.4%
c422c3ba-1848-4abf-9def-b470c11b3f6a 1
 
1.4%
47aac9db-0538-4b42-942a-cdbe0ab93ec4 1
 
1.4%
0c313bc5-0857-4776-b031-8d700e028db6 1
 
1.4%
dbaa0470-09b7-416b-897f-27145c17096c 1
 
1.4%
83c1b68a-c0df-4420-89bd-73fc4e5623bf 1
 
1.4%
da81a867-6185-4ea3-a193-03b560178764 1
 
1.4%
Other values (64) 64
86.5%
2025-07-21T11:07:36.421331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 296
 
11.1%
4 203
 
7.6%
9 173
 
6.5%
b 164
 
6.2%
8 162
 
6.1%
1 157
 
5.9%
a 156
 
5.9%
0 150
 
5.6%
6 147
 
5.5%
d 145
 
5.4%
Other values (7) 911
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
4 203
 
7.6%
9 173
 
6.5%
b 164
 
6.2%
8 162
 
6.1%
1 157
 
5.9%
a 156
 
5.9%
0 150
 
5.6%
6 147
 
5.5%
d 145
 
5.4%
Other values (7) 911
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
4 203
 
7.6%
9 173
 
6.5%
b 164
 
6.2%
8 162
 
6.1%
1 157
 
5.9%
a 156
 
5.9%
0 150
 
5.6%
6 147
 
5.5%
d 145
 
5.4%
Other values (7) 911
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
4 203
 
7.6%
9 173
 
6.5%
b 164
 
6.2%
8 162
 
6.1%
1 157
 
5.9%
a 156
 
5.9%
0 150
 
5.6%
6 147
 
5.5%
d 145
 
5.4%
Other values (7) 911
34.2%

samples__portions__slides__created_datetime
Date

Missing 

Distinct69
Distinct (%)93.2%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
Minimum2017-09-07 09:10:36.334974-05:00
Maximum2017-09-07 09:28:21.643925-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:36.461416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:36.502838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

samples__portions__slides__percent_tumor_nuclei
Real number (ℝ)

Missing 

Distinct10
Distinct (%)13.5%
Missing74
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean65.486486
Minimum20
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2025-07-21T11:07:36.531293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile36.5
Q150
median70
Q380
95-th percentile90
Maximum90
Range70
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.692381
Coefficient of variation (CV)0.27016843
Kurtosis-0.59044838
Mean65.486486
Median Absolute Deviation (MAD)10
Skewness-0.53159338
Sum4846
Variance313.02036
MonotonicityNot monotonic
2025-07-21T11:07:36.555522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80 21
 
14.2%
60 13
 
8.8%
70 10
 
6.8%
90 8
 
5.4%
50 8
 
5.4%
40 8
 
5.4%
30 3
 
2.0%
63 1
 
0.7%
73 1
 
0.7%
20 1
 
0.7%
(Missing) 74
50.0%
ValueCountFrequency (%)
20 1
 
0.7%
30 3
 
2.0%
40 8
 
5.4%
50 8
 
5.4%
60 13
8.8%
63 1
 
0.7%
70 10
6.8%
73 1
 
0.7%
80 21
14.2%
90 8
 
5.4%
ValueCountFrequency (%)
90 8
 
5.4%
80 21
14.2%
73 1
 
0.7%
70 10
6.8%
63 1
 
0.7%
60 13
8.8%
50 8
 
5.4%
40 8
 
5.4%
30 3
 
2.0%
20 1
 
0.7%

samples__portions__analytes__aliquots__aliquot_id
Text

Missing 

Distinct74
Distinct (%)100.0%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2025-07-21T11:07:36.653856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters2664
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st rowdb31e5fd-9a8f-4769-a382-5390482d11ce
2nd row21d4273e-9c12-437e-b9d7-986b521af63c
3rd rowedab950c-ec04-4fa5-af8e-52310049ce25
4th row3d9cb4ee-06ab-48bc-a686-26b571f2f484
5th rowaf514d51-0f2a-4dd8-8808-2f93a44383b2
ValueCountFrequency (%)
6289fb73-ce14-41f8-8a4e-a739914a66af 1
 
1.4%
d06234e1-8adf-4238-b623-9ac107c5c375 1
 
1.4%
21d4273e-9c12-437e-b9d7-986b521af63c 1
 
1.4%
edab950c-ec04-4fa5-af8e-52310049ce25 1
 
1.4%
3d9cb4ee-06ab-48bc-a686-26b571f2f484 1
 
1.4%
af514d51-0f2a-4dd8-8808-2f93a44383b2 1
 
1.4%
f16cea7a-0c63-4eba-9241-033d7d3b4552 1
 
1.4%
ccad70de-9d5c-4bcb-8878-745d8af0f809 1
 
1.4%
8db3cb5f-b77d-4034-826e-4528d8bb95eb 1
 
1.4%
612873ff-bb95-4fe9-a2d8-b51ec954212a 1
 
1.4%
Other values (64) 64
86.5%
2025-07-21T11:07:36.789038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 296
 
11.1%
4 216
 
8.1%
8 168
 
6.3%
9 164
 
6.2%
6 160
 
6.0%
b 151
 
5.7%
3 150
 
5.6%
a 150
 
5.6%
2 148
 
5.6%
5 142
 
5.3%
Other values (7) 919
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
4 216
 
8.1%
8 168
 
6.3%
9 164
 
6.2%
6 160
 
6.0%
b 151
 
5.7%
3 150
 
5.6%
a 150
 
5.6%
2 148
 
5.6%
5 142
 
5.3%
Other values (7) 919
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
4 216
 
8.1%
8 168
 
6.3%
9 164
 
6.2%
6 160
 
6.0%
b 151
 
5.7%
3 150
 
5.6%
a 150
 
5.6%
2 148
 
5.6%
5 142
 
5.3%
Other values (7) 919
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 296
 
11.1%
4 216
 
8.1%
8 168
 
6.3%
9 164
 
6.2%
6 160
 
6.0%
b 151
 
5.7%
3 150
 
5.6%
a 150
 
5.6%
2 148
 
5.6%
5 142
 
5.3%
Other values (7) 919
34.5%

samples__portions__analytes__aliquots__analyte_type
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.4%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
DNA
74 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters222
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDNA
2nd rowDNA
3rd rowDNA
4th rowDNA
5th rowDNA

Common Values

ValueCountFrequency (%)
DNA 74
50.0%
(Missing) 74
50.0%

Length

2025-07-21T11:07:36.823892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:36.840050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dna 74
100.0%

Most occurring characters

ValueCountFrequency (%)
D 74
33.3%
N 74
33.3%
A 74
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 74
33.3%
N 74
33.3%
A 74
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 74
33.3%
N 74
33.3%
A 74
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 74
33.3%
N 74
33.3%
A 74
33.3%

samples__portions__analytes__aliquots__updated_datetime
Categorical

Missing 

Distinct5
Distinct (%)6.8%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2018-08-23T20:55:48.618130-05:00
20 
2018-08-23T20:59:33.337271-05:00
20 
2018-08-23T21:03:20.063866-05:00
19 
2018-08-23T21:06:58.942298-05:00
14 
2018-08-23T20:51:55.199460-05:00
 
1

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2368
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row2018-08-23T21:06:58.942298-05:00
2nd row2018-08-23T20:55:48.618130-05:00
3rd row2018-08-23T21:06:58.942298-05:00
4th row2018-08-23T20:55:48.618130-05:00
5th row2018-08-23T21:03:20.063866-05:00

Common Values

ValueCountFrequency (%)
2018-08-23T20:55:48.618130-05:00 20
 
13.5%
2018-08-23T20:59:33.337271-05:00 20
 
13.5%
2018-08-23T21:03:20.063866-05:00 19
 
12.8%
2018-08-23T21:06:58.942298-05:00 14
 
9.5%
2018-08-23T20:51:55.199460-05:00 1
 
0.7%
(Missing) 74
50.0%

Length

2025-07-21T11:07:36.861066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-21T11:07:36.883547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-23t20:55:48.618130-05:00 20
27.0%
2018-08-23t20:59:33.337271-05:00 20
27.0%
2018-08-23t21:03:20.063866-05:00 19
25.7%
2018-08-23t21:06:58.942298-05:00 14
18.9%
2018-08-23t20:51:55.199460-05:00 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 503
21.2%
2 289
12.2%
8 235
9.9%
- 222
9.4%
: 222
9.4%
3 212
9.0%
1 169
 
7.1%
5 151
 
6.4%
6 92
 
3.9%
T 74
 
3.1%
Other values (4) 199
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 503
21.2%
2 289
12.2%
8 235
9.9%
- 222
9.4%
: 222
9.4%
3 212
9.0%
1 169
 
7.1%
5 151
 
6.4%
6 92
 
3.9%
T 74
 
3.1%
Other values (4) 199
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 503
21.2%
2 289
12.2%
8 235
9.9%
- 222
9.4%
: 222
9.4%
3 212
9.0%
1 169
 
7.1%
5 151
 
6.4%
6 92
 
3.9%
T 74
 
3.1%
Other values (4) 199
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 503
21.2%
2 289
12.2%
8 235
9.9%
- 222
9.4%
: 222
9.4%
3 212
9.0%
1 169
 
7.1%
5 151
 
6.4%
6 92
 
3.9%
T 74
 
3.1%
Other values (4) 199
 
8.4%

samples__portions__analytes__aliquots__submitter_id
Text

Missing 

Distinct74
Distinct (%)100.0%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
2025-07-21T11:07:36.969637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.364865
Min length13

Characters and Unicode

Total characters1063
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)100.0%

Sample

1st rowAD6786_aliquot
2nd rowAD16503_aliquot
3rd rowAD15350_aliquot
4th rowAD15243_aliquot
5th rowAD739_aliquot
ValueCountFrequency (%)
ad2375_aliquot 1
 
1.4%
ad4583_aliquot 1
 
1.4%
ad16503_aliquot 1
 
1.4%
ad15350_aliquot 1
 
1.4%
ad15243_aliquot 1
 
1.4%
ad739_aliquot 1
 
1.4%
ad4181_aliquot 1
 
1.4%
ad8732_aliquot 1
 
1.4%
ad5136_aliquot 1
 
1.4%
ad13968_aliquot 1
 
1.4%
Other values (64) 64
86.5%
2025-07-21T11:07:37.084347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 74
 
7.0%
l 74
 
7.0%
t 74
 
7.0%
o 74
 
7.0%
u 74
 
7.0%
D 74
 
7.0%
i 74
 
7.0%
q 74
 
7.0%
a 74
 
7.0%
_ 74
 
7.0%
Other values (10) 323
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 74
 
7.0%
l 74
 
7.0%
t 74
 
7.0%
o 74
 
7.0%
u 74
 
7.0%
D 74
 
7.0%
i 74
 
7.0%
q 74
 
7.0%
a 74
 
7.0%
_ 74
 
7.0%
Other values (10) 323
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 74
 
7.0%
l 74
 
7.0%
t 74
 
7.0%
o 74
 
7.0%
u 74
 
7.0%
D 74
 
7.0%
i 74
 
7.0%
q 74
 
7.0%
a 74
 
7.0%
_ 74
 
7.0%
Other values (10) 323
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 74
 
7.0%
l 74
 
7.0%
t 74
 
7.0%
o 74
 
7.0%
u 74
 
7.0%
D 74
 
7.0%
i 74
 
7.0%
q 74
 
7.0%
a 74
 
7.0%
_ 74
 
7.0%
Other values (10) 323
30.4%

samples__portions__analytes__aliquots__created_datetime
Date

Missing 

Distinct67
Distinct (%)90.5%
Missing74
Missing (%)50.0%
Memory size1.3 KiB
Minimum2017-06-01 11:29:08.512054-05:00
Maximum2017-06-01 11:57:52.302803-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-21T11:07:37.121350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:37.161234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-07-21T11:07:31.713672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:31.487320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:31.763134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-21T11:07:31.660085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-21T11:07:37.192927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
demographic__genderdemographic__updated_datetimediagnoses__age_at_diagnosisdiagnoses__classification_of_tumordiagnoses__site_of_resection_or_biopsysamples__portions__analytes__aliquots__updated_datetimesamples__portions__slides__percent_tumor_nucleisamples__portions__slides__updated_datetimesamples__sample_typesamples__tumor_descriptor
demographic__gender1.0000.0000.0000.0820.3640.0000.1910.0000.0820.082
demographic__updated_datetime0.0001.0000.2250.0000.3140.1040.0000.1790.0000.000
diagnoses__age_at_diagnosis0.0000.2251.0000.4610.3760.000-0.1070.0000.4610.461
diagnoses__classification_of_tumor0.0820.0000.4611.0000.9470.0000.0000.0000.8710.871
diagnoses__site_of_resection_or_biopsy0.3640.3140.3760.9471.0000.0000.0000.0000.9470.947
samples__portions__analytes__aliquots__updated_datetime0.0000.1040.0000.0000.0001.0000.0000.0000.0000.000
samples__portions__slides__percent_tumor_nuclei0.1910.000-0.1070.0000.0000.0001.0000.0000.0000.000
samples__portions__slides__updated_datetime0.0000.1790.0000.0000.0000.0000.0001.0000.0000.000
samples__sample_type0.0820.0000.4610.8710.9470.0000.0000.0001.0000.871
samples__tumor_descriptor0.0820.0000.4610.8710.9470.0000.0000.0000.8711.000

Missing values

2025-07-21T11:07:31.836844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-21T11:07:31.948783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-21T11:07:32.089091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

case_idprimary_sitedisease_typeupdated_datetimesubmitter_id_clinicalcreated_datetimeproject__project_id_clinicaldemographic__demographic_iddemographic__genderdemographic__updated_datetimedemographic__submitter_iddemographic__created_datetimediagnoses__morphologydiagnoses__submitter_iddiagnoses__created_datetimediagnoses__age_at_diagnosisdiagnoses__primary_diagnosisdiagnoses__classification_of_tumordiagnoses__updated_datetimediagnoses__diagnosis_iddiagnoses__site_of_resection_or_biopsysubmitter_id_bioproject__project_id_biosamples__tumor_descriptorsamples__updated_datetimesamples__sample_idsamples__submitter_idsamples__sample_typesamples__created_datetimesamples__portions__portion_idsamples__portions__analytes__analyte_idsamples__portions__slides__updated_datetimesamples__portions__slides__submitter_idsamples__portions__slides__slide_idsamples__portions__slides__created_datetimesamples__portions__slides__percent_tumor_nucleisamples__portions__analytes__aliquots__aliquot_idsamples__portions__analytes__aliquots__analyte_typesamples__portions__analytes__aliquots__updated_datetimesamples__portions__analytes__aliquots__submitter_idsamples__portions__analytes__aliquots__created_datetime
00171fffa-a648-4053-8c12-e0786450a030Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD67862017-06-01T08:55:29.659252-05:00FM-ADa97f73aa-6885-4c60-ac07-1af161177361male2019-04-26T16:29:13.804345-05:00AD6786_demographic2017-06-19T11:39:07.528224-05:008936/1AD6786_diagnosis2017-06-19T09:06:50.876331-05:0025495Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:14:57.959207-05:0058666d98-0fa4-4dba-9bd5-7a8ef139ea42Small intestine, NOSAD6786FM-ADMetastatic2023-10-13T14:28:47.406440-05:0003c0bf16-1381-4969-bb76-01cdfb0fba35AD6786_sampleMetastatic2017-06-01T10:43:05.432604-05:004b15a185-52a7-5706-90fc-98993bb9adb4NaN2018-08-23T19:39:28.814253-05:00AD6786_slided2a40825-5b8b-424f-b0a6-c06022151a972017-09-07T09:25:24.730157-05:0080.0NaNNaNNaNNaNNaN
10171fffa-a648-4053-8c12-e0786450a030Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD67862017-06-01T08:55:29.659252-05:00FM-ADa97f73aa-6885-4c60-ac07-1af161177361male2019-04-26T16:29:13.804345-05:00AD6786_demographic2017-06-19T11:39:07.528224-05:008936/1AD6786_diagnosis2017-06-19T09:06:50.876331-05:0025495Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:14:57.959207-05:0058666d98-0fa4-4dba-9bd5-7a8ef139ea42Small intestine, NOSAD6786FM-ADMetastatic2023-10-13T14:28:47.406440-05:0003c0bf16-1381-4969-bb76-01cdfb0fba35AD6786_sampleMetastatic2017-06-01T10:43:05.432604-05:0002e4e948-f51b-592c-bed7-66e2857c3afe7fd74dee-043e-5f4c-9d89-72bc0fa50a69NaNNaNNaNNaNNaNdb31e5fd-9a8f-4769-a382-5390482d11ceDNA2018-08-23T21:06:58.942298-05:00AD6786_aliquot2017-06-01T11:44:58.065503-05:00
20434ffff-a75c-4c7b-bfc0-87d54b7f836eOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD165032017-06-01T09:17:17.925080-05:00FM-AD54b9ae57-48ea-4376-81b5-840164116d33female2019-04-26T16:29:13.804345-05:00AD16503_demographic2017-06-19T11:47:30.676573-05:008936/1AD16503_diagnosis2017-06-16T16:29:19.297090-05:0011209Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:48:44.602668-05:00f4f536aa-7abe-48c2-8794-46a77d9e6e29Colon, NOSAD16503FM-ADMetastatic2023-10-13T14:28:47.406440-05:00b3b8e1b3-8bea-4223-8fd1-34fc55127207AD16503_sampleMetastatic2017-06-01T10:54:20.813999-05:00dff28d5e-7371-5553-8df8-3f0b7fccf162NaN2018-08-23T19:39:28.814253-05:00AD16503_slided03e146d-ae65-4ac4-a5c3-86bd6cf3638c2017-09-07T09:20:48.801662-05:0090.0NaNNaNNaNNaNNaN
30434ffff-a75c-4c7b-bfc0-87d54b7f836eOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD165032017-06-01T09:17:17.925080-05:00FM-AD54b9ae57-48ea-4376-81b5-840164116d33female2019-04-26T16:29:13.804345-05:00AD16503_demographic2017-06-19T11:47:30.676573-05:008936/1AD16503_diagnosis2017-06-16T16:29:19.297090-05:0011209Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:48:44.602668-05:00f4f536aa-7abe-48c2-8794-46a77d9e6e29Colon, NOSAD16503FM-ADMetastatic2023-10-13T14:28:47.406440-05:00b3b8e1b3-8bea-4223-8fd1-34fc55127207AD16503_sampleMetastatic2017-06-01T10:54:20.813999-05:003a8c21ba-9e7c-5d06-8daf-e62666f4aa70d4cd439c-5490-5930-a777-35a533313282NaNNaNNaNNaNNaN21d4273e-9c12-437e-b9d7-986b521af63cDNA2018-08-23T20:55:48.618130-05:00AD16503_aliquot2017-06-01T11:54:28.579919-05:00
40946a9c1-d7a8-4e85-89a9-8ba47fde4f1bOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD153502017-06-01T09:05:19.758776-05:00FM-AD79c8986d-3158-4f2b-93ed-ede1de151cd2female2019-04-26T16:29:13.804345-05:00AD15350_demographic2017-06-19T11:41:31.395900-05:008936/1AD15350_diagnosis2017-06-16T16:21:20.304647-05:0027339Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T12:49:55.289377-05:00c15ee7ef-99af-4fcf-9329-408ddffc99c3Connective, subcutaneous and other soft tissues, NOSAD15350FM-ADMetastatic2023-10-13T14:28:47.406440-05:00337f28e1-0c6f-4f4b-b594-13192df2dfbbAD15350_sampleMetastatic2017-06-01T10:46:29.991257-05:00a43034e5-2fa4-51d2-9aa0-b9c1014bf634NaN2018-08-23T19:39:28.814253-05:00AD15350_slidec9072edc-7ffc-46dc-b365-e1c8503bfadf2017-09-07T09:13:02.282550-05:0063.0NaNNaNNaNNaNNaN
50946a9c1-d7a8-4e85-89a9-8ba47fde4f1bOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD153502017-06-01T09:05:19.758776-05:00FM-AD79c8986d-3158-4f2b-93ed-ede1de151cd2female2019-04-26T16:29:13.804345-05:00AD15350_demographic2017-06-19T11:41:31.395900-05:008936/1AD15350_diagnosis2017-06-16T16:21:20.304647-05:0027339Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T12:49:55.289377-05:00c15ee7ef-99af-4fcf-9329-408ddffc99c3Connective, subcutaneous and other soft tissues, NOSAD15350FM-ADMetastatic2023-10-13T14:28:47.406440-05:00337f28e1-0c6f-4f4b-b594-13192df2dfbbAD15350_sampleMetastatic2017-06-01T10:46:29.991257-05:00daf624d5-a5ed-5195-9760-10ec810f814aa5c93ec7-4388-5ee0-818d-821f0339cfc9NaNNaNNaNNaNNaNedab950c-ec04-4fa5-af8e-52310049ce25DNA2018-08-23T21:06:58.942298-05:00AD15350_aliquot2017-06-01T11:47:24.122889-05:00
609aacc20-afeb-4bed-96f1-cd709b0741abOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD152432017-06-01T09:02:23.977439-05:00FM-ADb8b726d9-0e5d-4fec-be03-7fb446154a55female2019-04-26T16:29:13.804345-05:00AD15243_demographic2017-06-19T11:40:03.744071-05:008936/1AD15243_diagnosis2017-06-19T08:39:55.791384-05:0023423Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:51:04.916029-05:00ff9c4ba7-1198-4466-9489-a3e15a09d77cSmall intestine, NOSAD15243FM-ADMetastatic2023-10-13T14:28:47.406440-05:00be3d7a06-4ec5-4b44-90d1-b7b2552a9831AD15243_sampleMetastatic2017-06-01T10:44:35.944545-05:00f55a1a11-e887-58bb-bb25-fc03699779e4NaN2018-08-23T19:39:28.814253-05:00AD15243_slideb4498a81-e7dc-4cd7-bac0-396120453e3a2017-09-07T09:11:20.841694-05:0070.0NaNNaNNaNNaNNaN
709aacc20-afeb-4bed-96f1-cd709b0741abOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD152432017-06-01T09:02:23.977439-05:00FM-ADb8b726d9-0e5d-4fec-be03-7fb446154a55female2019-04-26T16:29:13.804345-05:00AD15243_demographic2017-06-19T11:40:03.744071-05:008936/1AD15243_diagnosis2017-06-19T08:39:55.791384-05:0023423Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:51:04.916029-05:00ff9c4ba7-1198-4466-9489-a3e15a09d77cSmall intestine, NOSAD15243FM-ADMetastatic2023-10-13T14:28:47.406440-05:00be3d7a06-4ec5-4b44-90d1-b7b2552a9831AD15243_sampleMetastatic2017-06-01T10:44:35.944545-05:0047150fa5-f192-5028-acf5-ff06dcf1a886e2b588e1-c10d-5a89-8514-b78317487523NaNNaNNaNNaNNaN3d9cb4ee-06ab-48bc-a686-26b571f2f484DNA2018-08-23T20:55:48.618130-05:00AD15243_aliquot2017-06-01T11:45:42.278246-05:00
80a373e5b-7df2-4a37-b04b-f7b0751ca6b5Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD7392017-06-01T09:03:44.137960-05:00FM-AD2dbac075-40eb-4a03-b98e-34aded6819bcmale2019-04-26T16:29:13.804345-05:00AD739_demographic2017-06-19T11:43:12.941991-05:008936/1AD739_diagnosis2017-06-19T09:19:05.724696-05:0021331Gastrointestinal stromal tumor, NOSUnknown2019-07-10T13:13:41.595275-05:00541fad7d-bf7a-4366-836a-11245198496eNot ReportedAD739FM-ADUnknown2023-10-13T14:28:47.406440-05:00a9684a01-d037-4444-929e-acd6bfa2a8cfAD739_sampleTumor2017-06-01T10:48:56.774852-05:00463e03ef-179d-5582-95a0-2969b76e129cNaN2018-08-23T19:39:28.814253-05:00AD739_slidec422c3ba-1848-4abf-9def-b470c11b3f6a2017-09-07T09:12:23.217844-05:0080.0NaNNaNNaNNaNNaN
90a373e5b-7df2-4a37-b04b-f7b0751ca6b5Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD7392017-06-01T09:03:44.137960-05:00FM-AD2dbac075-40eb-4a03-b98e-34aded6819bcmale2019-04-26T16:29:13.804345-05:00AD739_demographic2017-06-19T11:43:12.941991-05:008936/1AD739_diagnosis2017-06-19T09:19:05.724696-05:0021331Gastrointestinal stromal tumor, NOSUnknown2019-07-10T13:13:41.595275-05:00541fad7d-bf7a-4366-836a-11245198496eNot ReportedAD739FM-ADUnknown2023-10-13T14:28:47.406440-05:00a9684a01-d037-4444-929e-acd6bfa2a8cfAD739_sampleTumor2017-06-01T10:48:56.774852-05:00b9869132-7c25-5fad-ad22-0301ce6ae6f717fc7cb5-3183-58a8-8211-d9df5f848791NaNNaNNaNNaNNaNaf514d51-0f2a-4dd8-8808-2f93a44383b2DNA2018-08-23T21:03:20.063866-05:00AD739_aliquot2017-06-01T11:49:35.606793-05:00
case_idprimary_sitedisease_typeupdated_datetimesubmitter_id_clinicalcreated_datetimeproject__project_id_clinicaldemographic__demographic_iddemographic__genderdemographic__updated_datetimedemographic__submitter_iddemographic__created_datetimediagnoses__morphologydiagnoses__submitter_iddiagnoses__created_datetimediagnoses__age_at_diagnosisdiagnoses__primary_diagnosisdiagnoses__classification_of_tumordiagnoses__updated_datetimediagnoses__diagnosis_iddiagnoses__site_of_resection_or_biopsysubmitter_id_bioproject__project_id_biosamples__tumor_descriptorsamples__updated_datetimesamples__sample_idsamples__submitter_idsamples__sample_typesamples__created_datetimesamples__portions__portion_idsamples__portions__analytes__analyte_idsamples__portions__slides__updated_datetimesamples__portions__slides__submitter_idsamples__portions__slides__slide_idsamples__portions__slides__created_datetimesamples__portions__slides__percent_tumor_nucleisamples__portions__analytes__aliquots__aliquot_idsamples__portions__analytes__aliquots__analyte_typesamples__portions__analytes__aliquots__updated_datetimesamples__portions__analytes__aliquots__submitter_idsamples__portions__analytes__aliquots__created_datetime
138faaa8f08-cfc4-4aec-a350-bfaf7c6458ebOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD54892017-06-01T08:52:15.070536-05:00FM-ADb8b5fa06-cf47-4729-aef9-e2442ca80f2dmale2019-04-26T16:29:13.804345-05:00AD5489_demographic2017-06-19T11:36:49.607637-05:008936/1AD5489_diagnosis2017-06-16T16:10:50.079608-05:0021530Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:41:05.837195-05:00d9deaa13-57e0-4d23-ac64-7ac394b3f447Stomach, NOSAD5489FM-ADMetastatic2023-10-13T14:28:47.406440-05:00c734076f-d5a1-49d9-9823-1eb272341fb6AD5489_sampleMetastatic2017-06-01T10:40:58.545721-05:0081797006-9da2-5cb7-98c3-b818b8685e7bNaN2018-08-23T19:39:28.814253-05:00AD5489_slideea6883ce-598c-40be-855f-33bef00077952017-09-07T09:23:23.822379-05:0040.0NaNNaNNaNNaNNaN
139faaa8f08-cfc4-4aec-a350-bfaf7c6458ebOther and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD54892017-06-01T08:52:15.070536-05:00FM-ADb8b5fa06-cf47-4729-aef9-e2442ca80f2dmale2019-04-26T16:29:13.804345-05:00AD5489_demographic2017-06-19T11:36:49.607637-05:008936/1AD5489_diagnosis2017-06-16T16:10:50.079608-05:0021530Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:41:05.837195-05:00d9deaa13-57e0-4d23-ac64-7ac394b3f447Stomach, NOSAD5489FM-ADMetastatic2023-10-13T14:28:47.406440-05:00c734076f-d5a1-49d9-9823-1eb272341fb6AD5489_sampleMetastatic2017-06-01T10:40:58.545721-05:003c88a21c-234a-5a8c-950d-527b16608d2745370911-b423-5f65-bb14-53e4b9c2a204NaNNaNNaNNaNNaN57635bdc-b201-4656-b895-bc2b84ceda56DNA2018-08-23T20:59:33.337271-05:00AD5489_aliquot2017-06-01T11:43:05.235321-05:00
140fae9b190-d0d8-42ac-88b7-0c94f301ff23Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD146312017-06-01T09:01:48.816045-05:00FM-AD9a144e57-2fda-4ecb-a681-ab4c519d909bmale2019-04-26T16:28:42.290058-05:00AD14631_demographic2017-06-19T11:39:47.599542-05:008936/1AD14631_diagnosis2017-06-16T16:16:26.732354-05:0017692Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T12:53:48.888479-05:00e5292289-301f-4bea-8b51-5f121116c940Connective, subcutaneous and other soft tissues, NOSAD14631FM-ADMetastatic2023-10-13T14:28:47.406440-05:003eeda0de-5431-4911-8316-a6be9f4dcd2eAD14631_sampleMetastatic2017-06-01T10:44:13.351747-05:00da8c884f-649a-5b01-9e00-10cea678f4daNaN2018-08-23T19:28:09.950838-05:00AD14631_slide134d10bd-dce0-407d-83ea-666042bd67f22017-09-07T09:10:58.261128-05:0080.0NaNNaNNaNNaNNaN
141fae9b190-d0d8-42ac-88b7-0c94f301ff23Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD146312017-06-01T09:01:48.816045-05:00FM-AD9a144e57-2fda-4ecb-a681-ab4c519d909bmale2019-04-26T16:28:42.290058-05:00AD14631_demographic2017-06-19T11:39:47.599542-05:008936/1AD14631_diagnosis2017-06-16T16:16:26.732354-05:0017692Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T12:53:48.888479-05:00e5292289-301f-4bea-8b51-5f121116c940Connective, subcutaneous and other soft tissues, NOSAD14631FM-ADMetastatic2023-10-13T14:28:47.406440-05:003eeda0de-5431-4911-8316-a6be9f4dcd2eAD14631_sampleMetastatic2017-06-01T10:44:13.351747-05:00325d7dc7-1d40-5f10-bf00-5370825ce02a265f7ffc-f013-5e95-a88d-76c955f5bc19NaNNaNNaNNaNNaN9b3ade48-ca85-46ae-80a9-fad30cadc0d0DNA2018-08-23T21:03:20.063866-05:00AD14631_aliquot2017-06-01T11:45:22.326730-05:00
142fbcb378e-b5f2-4c5f-b9a2-c14b5d620198Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD172762017-06-01T09:21:27.912012-05:00FM-AD4180e6f6-7658-4fed-8e2b-b428601cf945male2019-04-26T16:29:13.804345-05:00AD17276_demographic2017-06-19T11:50:11.754748-05:008936/1AD17276_diagnosis2017-06-16T16:33:56.668086-05:0021521Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:17:59.975541-05:00645b7fbd-4af7-4fce-801f-cf831b5b473eStomach, NOSAD17276FM-ADMetastatic2023-10-13T14:28:47.406440-05:00250d9ac1-c863-47be-b862-1ae393cbf58bAD17276_sampleMetastatic2017-06-01T10:56:59.753056-05:00d50230ce-d193-5292-931a-bc8c48a5254bNaN2018-08-23T19:39:28.814253-05:00AD17276_slideb2c68271-2329-48e0-bfa9-21b428fdbb512017-09-07T09:23:13.071710-05:0070.0NaNNaNNaNNaNNaN
143fbcb378e-b5f2-4c5f-b9a2-c14b5d620198Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD172762017-06-01T09:21:27.912012-05:00FM-AD4180e6f6-7658-4fed-8e2b-b428601cf945male2019-04-26T16:29:13.804345-05:00AD17276_demographic2017-06-19T11:50:11.754748-05:008936/1AD17276_diagnosis2017-06-16T16:33:56.668086-05:0021521Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:17:59.975541-05:00645b7fbd-4af7-4fce-801f-cf831b5b473eStomach, NOSAD17276FM-ADMetastatic2023-10-13T14:28:47.406440-05:00250d9ac1-c863-47be-b862-1ae393cbf58bAD17276_sampleMetastatic2017-06-01T10:56:59.753056-05:008faf329e-4e85-546f-92aa-45c4ce515457ab56cd3e-1a71-56af-8338-0924fdde7c4cNaNNaNNaNNaNNaN04e08a08-8a14-4282-be52-afac25c19b4bDNA2018-08-23T20:55:48.618130-05:00AD17276_aliquot2017-06-01T11:56:58.517640-05:00
144fd9c738b-ac3a-4a95-aa1c-30b37254f639Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD169402017-06-01T09:18:46.512909-05:00FM-AD528ae26a-13fb-4249-a364-5fc97f9afd1cmale2019-04-26T16:29:13.804345-05:00AD16940_demographic2017-06-19T11:48:28.595638-05:008936/1AD16940_diagnosis2017-06-16T16:30:51.289982-05:0015947Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:45:16.307760-05:00ea2fcea4-4176-4af0-9289-3c0264819d78Stomach, NOSAD16940FM-ADMetastatic2023-10-13T14:28:47.406440-05:00aed28a91-a8e3-40d9-b916-e8b45b34552eAD16940_sampleMetastatic2017-06-01T10:55:17.066143-05:003e43f851-ed45-51d2-99fb-75d7ea25d06eNaN2018-08-23T19:35:48.363089-05:00AD16940_slide7f4d6228-7d5b-40fc-aa0f-666f0e1d47892017-09-07T09:21:46.324795-05:0060.0NaNNaNNaNNaNNaN
145fd9c738b-ac3a-4a95-aa1c-30b37254f639Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD169402017-06-01T09:18:46.512909-05:00FM-AD528ae26a-13fb-4249-a364-5fc97f9afd1cmale2019-04-26T16:29:13.804345-05:00AD16940_demographic2017-06-19T11:48:28.595638-05:008936/1AD16940_diagnosis2017-06-16T16:30:51.289982-05:0015947Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:45:16.307760-05:00ea2fcea4-4176-4af0-9289-3c0264819d78Stomach, NOSAD16940FM-ADMetastatic2023-10-13T14:28:47.406440-05:00aed28a91-a8e3-40d9-b916-e8b45b34552eAD16940_sampleMetastatic2017-06-01T10:55:17.066143-05:001c1fced6-0acb-5e3b-ba41-554583f704477d9dd028-7a62-53df-80aa-8041e06bcd72NaNNaNNaNNaNNaN91acc826-a289-421a-86d1-a381116f0c0eDNA2018-08-23T21:03:20.063866-05:00AD16940_aliquot2017-06-01T11:55:21.371748-05:00
146ffb0514c-62a4-4970-b825-d49a0e570550Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD59392017-06-01T08:51:55.483498-05:00FM-AD8a912d20-d7b0-4e46-8272-1c9951297a20female2019-04-26T16:29:13.804345-05:00AD5939_demographic2017-06-19T11:36:31.066693-05:008936/1AD5939_diagnosis2017-06-16T16:10:22.802829-05:0024008Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:37:24.545524-05:00ca8c8b1e-1b28-4676-adbf-ea2cb680bcc2Specified parts of peritoneumAD5939FM-ADMetastatic2023-10-13T14:28:47.406440-05:0063811b9a-b537-4786-92a7-d2f4417e48d0AD5939_sampleMetastatic2017-06-01T10:40:49.120715-05:00ce2c12b9-a411-5843-ad78-47b7523bee8eNaN2018-08-23T19:39:28.814253-05:00AD5939_slidec8911db8-8075-4346-bacf-90e6e4e4e0de2017-09-07T09:23:12.480002-05:0060.0NaNNaNNaNNaNNaN
147ffb0514c-62a4-4970-b825-d49a0e570550Other and ill-defined digestive organsComplex Mixed and Stromal Neoplasms2018-10-25T11:34:27.425461-05:00AD59392017-06-01T08:51:55.483498-05:00FM-AD8a912d20-d7b0-4e46-8272-1c9951297a20female2019-04-26T16:29:13.804345-05:00AD5939_demographic2017-06-19T11:36:31.066693-05:008936/1AD5939_diagnosis2017-06-16T16:10:22.802829-05:0024008Gastrointestinal stromal tumor, NOSmetastasis2019-07-10T13:37:24.545524-05:00ca8c8b1e-1b28-4676-adbf-ea2cb680bcc2Specified parts of peritoneumAD5939FM-ADMetastatic2023-10-13T14:28:47.406440-05:0063811b9a-b537-4786-92a7-d2f4417e48d0AD5939_sampleMetastatic2017-06-01T10:40:49.120715-05:00118f8189-58b9-52f0-8ec6-41aea0f5069941be764d-f76e-5137-a484-1ac30c0e4c78NaNNaNNaNNaNNaNfabad341-23ab-4178-8a06-429a64054bd1DNA2018-08-23T21:06:58.942298-05:00AD5939_aliquot2017-06-01T11:42:55.419591-05:00